SurvDC: Semiparametric Estimation of the Survival Function under...

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Semiparametric Estimation of the Survival Function under Dependent Censoring

Description

Provide semiparametric approaches that can be used to model right-censored survival data under dependent censoring (without covariates). The copula-based approach is adopted and there is no need to explicitly specify the association parameter. One of the margins can be modeled nonparametrically. As a byproduct, both marginal distributions of survival and censoring times can be considered as fully parametric. The existence of a cured fraction concerning survival time can also be taken into consideration.

Usage

SurvDC(
  yobs,
  delta,
  tm = NULL,
  copfam = "frank",
  margins = list(survfam = NULL, censfam = "lnorm"),
  cure = FALSE,
  Var = list(do = TRUE, nboot = 200, level = 0.05),
  control = list(maxit = 300, eps = 1e-06, trace = TRUE, ktau.inits = NULL)
)

Arguments

yobs	a numeric vector that indicated the observed survival times.
delta	a numeric vector that stores the right-censoring indicators.
tm	a numeric vector that contains interested non-negative time points at which the survival probabilities will be evluated. Note that if we omit the definition of this argument (the default value becomes NULL), our function will automatically output survival probabilities at all oberserved time points, that is, yobs.
copfam	a character string that specifies the copula family. Currently, it supports Archimedean copula families, including "frank" (the default value), "clayton", "gumbel", and "joe". The degenerated independent censoring case can be considered as well by setting "indep". (other options will be added in the near future!)
margins	a list used to define the distribution structures of both the survival and censoring margins. Specifically, it contains the following elements: survfam a character string that defines the assumed distribution for the survival time random variable, including "lnorm" for log-normal distribution, "weibull" for weibull distribution (other options will be added in the near future). censfam a character string that defines the assumed distribution for the censoring time random variable, and the details are the same as those shown in survfam. survtrunc a positive numeric value thats denotes the value of truncation for the assumed distribution, that is, survfam. censtrunc a positive numeric value thats denotes the value of truncation for the assumed distribution, that is, censfam. Note if one of the marginal distributions should be modeled nonparametrically, one can let the corresponding argument to be NULL directly. For example if a semiparametric framework that defines the survival margin to be nonparametric and the censoring margin to be parametric, say log-normal, is desired, we can let survfam = NULL and censfam = "lnorm", which is indeed the default value. Furthermore, if no truncation is imposed in survfam (or censfam), one can directly omit the specification of survtrunc (or censtrunc), which is the default specification. We also remark here that when a cured fraction is included (cure = TRUE), if survfam is not NULL and survtrunc = NULL, we will automatically let survtrunc to be max(yobs). If we wants to model the data with a non-truncated survival distribution when there is a cured fraction, we can set survtrunc = Inf.
cure	a logical value that indicates whether the existence of a cured fraction should be considered.
Var	a list that controls the execution of the bootstrap for variance estimation, and it contains two elements: do is a logical value with default FALSE to tell the function whether the boostrap-based variances should be calculated; nboot is a numeric integer that specifies the number of bootstrap samples.
control	indicates more detailed control of the underlying model fitting procedures. It is a list of the following three arguments: maxit a positive integer that denotes the maximum iteration number in optimization. The default value is 300. eps a positive small numeric value that denotes the tolerance for convergence. The default value is 1e-6. trace a logical value that judges whereh the tracing information on the progress of the model fitting should be produced. The default value if TRUE. ktau.inits a numeric vector that contains initial values of the Kendall's tau. The default value is NULL, meaning that a grids of initial values will be automatically generated within our function.

Details

This unified function provide approaches that can be used to model right-censored survival data under dependent censoring (without covariates). Various specifications of marginal distributions can be considered by choosing different combinations of the provided arguments. Generally speaking, the following two scenarios are what we mainly focused on:

nonparametric survival margin and parametric censoring margin (without cure)

survfam = NULL, censfam is not NULL and cure = FALSE.

nonparametric survival margin and parametric censoring margin (with cure)

survfam = NULL, censfam is not NULL and cure = TRUE.

As byproducts, several other scenarios (the distribution of the underlying survival time is not nonparametric but fully parametric) can also be considered by this R function:

parametric survival and censoring margins (without cure)

both survfam and censfam are not NULL and cure = FALSE.

parametric survival and censoring margins (with cure)

both survfam and censfam are not NULL and cure = TRUE.

parametric survival margin and nonparametric censoring margin (without cure)

survfam is not NULL, censfam = NULL and cure = FALSE.

Furthermore, one might expect that a scenario with "parametric survival margin and nonparametric censoring margin (with cure)" can also be included. Indeed, it can be done based on: survfam is not NULL, censfam = NULL and cure = TRUE. However, from a theoretical perspective of view, whether this type of modeling is reasonable or not still needs further investigations.

We emphasize that the first scenario (in byproducts) has also be considered in another function of this package. Specifically, the scenario of "parametric survival margin and nonparametric censoring margin (without cure)" can be fitted based on ParamCop(). However, the default joint modeling of survival and censoring times are based on their joint survival function in line with the semiparametric case (instead of modeling joint distribution function directly as in Czado and Van Keilegom (2023) <doi:10.1093/biomet/asac067>), but the idea of estimation methodology are exactly the same.

@references Czado and Van Keilegom (2023). Dependent censoring based on parametric copulas. Biometrika, 110(3), 721-738. @references Delhelle and Van Keilegom (2024). Copula based dependent censoring in cure models. TEST (to appear). @references Ding and Van Keilegom (2024). Semiparametric estimation of the survival function under dependent censoring (in preparation).

Value

A list of fitted results is returned. Within this outputted list, the following elements can be found:

probs

survival probabilities of the survial margin at tm.

ktau

Kendall's tau.

parapar

estimation of all parameters (except Kendall's tau) contained in the parametric part.

GoF

goodness-of-test results.

curerate

cure rate. If cure = FALSE, it is NULL.

Examples

#----------------------------------------------------------#
# Basic preparations before running subsequent examples ####
#----------------------------------------------------------#

# library necessary packages

#------------------------------------------------------------------------#
# simulated data from Frank copula log-Normal margins (without cure)
#------------------------------------------------------------------------#

# generate the simulated data

# - the sample size of the generated data
n <- 1000

# information on the used copula
copfam.true <- "frank"
ktau.true <- 0.5
coppar.true <- 5.74

# parameters of the underlying log-normal marginal distributions
survpar.true <- c(2.20,1.00)
censpar.true <- c(2.20,0.25)

# - true underlying survival and censoring times
set.seed(1)
u.TC <- copula::rCopula(
  n        = n,
  copula   = copula::archmCopula(
    family = copfam.true,
    param  = coppar.true,
    dim    = 2
  )
)
yobs.T <- qlnorm(1-u.TC[,1],survpar.true[1],survpar.true[2])
yobs.C <- qlnorm(1-u.TC[,2],censpar.true[1],censpar.true[2])

# observations
yobs  <- pmin(yobs.T,yobs.C)
delta <- as.numeric(yobs.T<=yobs.C)
cat("censoring rate is", mean(1-delta))

# model the data under different scenarios

# scenario 1: nonparametric survival margin and parametric censoring margin
set.seed(1)
sol.scenario1 <- SurvDC(
  yobs    = yobs,
  delta   = delta,
  tm      = quantile(yobs, c(0.25,0.50,0.75)),
  copfam  = copfam.true,
  margins = list(survfam = NULL, censfam = "lnorm"),
  Var     = list(do = FALSE, nboot = 50)
)
sol.scenario1$probs
sol.scenario1$ktau
sol.scenario1$parapar

# scenario 2: parametric survival and censoring margins
set.seed(1)
sol.scenario2 <- SurvDC(
  yobs    = yobs,
  delta   = delta,
  tm      = quantile(yobs, c(0.25,0.50,0.75)),
  copfam  = copfam.true,
  margins = list(survfam = "lnorm", censfam = "lnorm"),
  Var     = list(do = FALSE, nboot = 50)
)
sol.scenario2$probs
sol.scenario2$ktau
sol.scenario2$parapar

# scenario 3: parametric survival margin and nonparametric censoring margin
set.seed(1)
sol.scenario3 <- SurvDC(
  yobs    = yobs,
  delta   = delta,
  tm      = quantile(yobs, c(0.25,0.50,0.75)),
  copfam  = copfam.true,
  margins = list(survfam = "lnorm", censfam = NULL),
  Var     = list(do = FALSE, nboot = 50)
)
sol.scenario3$probs
sol.scenario3$ktau
sol.scenario3$parapar

#------------------------------------------------------------------------
# simulated data from Frank copula log-Normal margins (with cure)
#------------------------------------------------------------------------

# generate the simulated data

#  true underlying cure rate
curerate.true <- 0.2

# true underlying survival and censoring times
set.seed(1)
u.TC <- copula::rCopula(
  n        = n,
  copula   = copula::archmCopula(
    family = copfam.true,
    param  = coppar.true,
    dim    = 2
  )
)
yobs.T <- sapply(u.TC[,1],function(uT){
  if(uT<=curerate.true){ val <- Inf }else{
    val <- EnvStats::qlnormTrunc((1-uT)/(1-curerate.true),survpar.true[1],survpar.true[2],0,15)
  }
  return(val)
})
yobs.C <- qlnorm(1-u.TC[,2],censpar.true[1],censpar.true[2])
cat("cure rate is",mean(yobs.T==Inf))

#  observations
yobs  <- pmin(yobs.T,yobs.C)
delta <- as.numeric(yobs.T<=yobs.C)
cat("censoring rate is",mean(1-delta))

# model the data under different scenarios (with cure)

# scenario 4: parametric survival and censoring margins
set.seed(1)
sol.scenario4 <- SurvDC(
  yobs    = yobs,
  delta   = delta,
  tm      = quantile(yobs, c(0.25,0.50,0.75)),
  copfam  = copfam.true,
  margins = list(survfam = "lnorm", censfam = "lnorm"),
  Var     = list(do = FALSE, nboot = 50),
  cure    = TRUE
)
sol.scenario4$probs
sol.scenario4$ktau
sol.scenario4$parapar
sol.scenario4$curerate

# scenario 5: nonparametric survival margin and parametric censoring margin
set.seed(1)
sol.scenario5 <- SurvDC(
  yobs    = yobs,
  delta   = delta,
  tm      = quantile(yobs, c(0.25,0.50,0.75)),
  copfam  = copfam.true,
  margins = list(survfam = NULL, censfam = "lnorm"),
  Var     = list(do = FALSE, nboot = 50),
  cure    = TRUE
)
sol.scenario5$probs
sol.scenario5$ktau
sol.scenario5$parapar
sol.scenario5$curerate

depCensoring documentation built on April 4, 2025, 1:52 a.m.